1D Inversion of Time-Domain Electromagnetic Data with Induced Polarization Effects for a Sea-Floor Hydrothermal Deposit
Bibliographic record
Abstract
Summary Seafloor hydrothermal deposits are polymetallic massive sulfide ore deposits formed by the precipitation of metal components contained in hot water ejected from the seafloor. The sea depth is 700–2000m. Ore bodies extend hundreds of meters horizontally and tens of meters vertically. Ore bodies are exposed on the sea floor. Some lab-based petrophysics study indicates that resistivity and chargeability are diagnostic physical properties, even compared with seawater (3.0–3.5 S/m). Time-domain electromagnetic methods (TEM) are sensitive to variations in resistivity. WISTEM (Waseda integrated seafloor time-domain electromagnetic exploration) surveys have been conducted in several areas. Negative transients, which are due to induced polarization effects (IP), have been observed for data collected over known deposits. It is important to understand the system response to invert these data. The pressure vessel (PV), which contains the transmitter and receivers, can impact the data. We use numerical simulations to quantify these effects, and we develop a workflow for estimating a linear filter which captures the effects of the PV. This filter will then be used in subsequent simulations and inversions. Finally, we perform one-dimensional time-domain IP inversion of the field data. The estimated resistivity and IP parameters agree with physical property measurements from the area.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".